Comcast Cable Communications, LLC

Hans Sayyadi

Jan Neumann

Oliver Jojic

Ehsan Younessian

Amit Bagga

Brian Curtis

Jeanine Heck

Larry Cynkin

Mevan Samarasinghe

Muthu Manickam

Erik Schwartz

Stefan Deichmann

Vamsi Potluru

Nima Bina

Methods and apparatus for generating personalized menus and a media content interface are provided. In one example, different device specific media asset lists are generated at different user devices for the same user based on the media consumption history at each device. In another example, media asset listings are displayed for multicast and on-demand media assets determined to be of interest to the user. An on-demand media asset is listed at a time when no other multicast media assets are listed. In another example, media asset listings for successive episodes of a media series are displayed in sequential order in response to an input to view successive episodes of a media series. In another example, a text search for media content is performed in which the search priority of media provider results is decreased relative to the search priority of media asset results as additional characters are added to a search string.

Personalized menus and media content interface

Aspects described herein describe selecting advertisements for insertion into video content. An advertisement may be selected based on when content was previously consumed.

Advertisement selection for insertion into video content based on viewing habits

Systems and methods are described for improving content classifications and metadata for a plurality of content items. The content items may comprise various forms of media content. A content recommendation processing system may automatically annotate and/or classify content items with a plurality of metadata tag information describing content items. The disclosed system and methods may also determine and/or assign applicability scores indicating a level or degree of suitability for annotating a content item with particular metadata tag information, and may utilize applicability scores to provide content to calibrate metadata information for a plurality of content items, and further to provide content recommendations to users.

Content recommendation system with weighted metadata annotations

According to some aspects, methods and systems may include receiving, by a computing device, metadata identifying an event occurring in a video program, and determining an expected motion of objects in the identified event. The methods and systems may further include analyzing motion energy in the video program to identify video frames in which the event occurs, and storing information identifying the video frames in which the event occurs.

Identifying one or more events in content

A user device (e.g., voice assistant device, voice enabled device, smart device, computing device, etc.) may receive/detect audio content (e.g., speech, etc.) that includes a wake word and/or words similar to a wake word. The user device may require a wake word, a portion of the wake word, or words similar to the wake word to be detected prior to interacting with a user. The user device may, based on characteristics of the audio content, determine if the audio content originates from an authorized user. The user device may decrease and/or increase scrutiny applied to wake word detection based on whether audio content originates from an authorized user.

Methods and systems for determining a wake word

Methods, systems, and apparatuses are described for a recommendation ranking system that uses viewing and/or browsing data of content items available from a communication system received from users of the system. The viewing and browsing data may be obtained from the users through different availability periods having varying price points for viewing the content items. Weighted values may be applied to the viewing and browsing data based on the price point or availability period of the content items associated with the collected data. A popularity index for the content items of the communication system may be determined based on the adjusted viewing and browsing data and a ranking of recommendations for content items may be generated based at least on the popularity index of the content items.

Prioritized content selection and delivery

Aspects described herein describe providing content recommendations such as, for example, recommendations for video content. A content recommendation may be based on when content was previously consumed.

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Washington, DC, United States of America

Hans Sayyadi